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Learn how to read GPU benchmarks and get the most bang for your buck.

With DOCA 2.7 released, get ready to explore the new Remote Direct Memory Access (RDMA) functionalities controlled by a GPU CUDA kernel with DOCA GPUNetIO and a performance comparison with the performance test (perftest) microbenchmarks. https://nvda.ws/45nGovU

GPU Performance- reduce Instruction Cache Misses

Democratizing #longcontext One GPU at a Time 😏 Let's face it: not every dev has a secret lair filled with endless GPUs. But who needs a server farm when you've got #Jamba? 140K context windows on a single GPU? Yes, please! Jamba isn't just breaking barriers; it's smashing them to bits. Now, every developer can harness the power of long context without breaking the bank (or the laws of physics). Keep in mind: Innovation isn't about having the most resources; it's about maximizing the ones you have. Are you ready to squeeze every drop of potential from your GPU? Let's Jamba! 🎉 AI21 Labs Olivia Gorvy Adina Noble Asaff Zamir Ana Turchetti-Maia, PhD Netanel Levenson Ori Shapira Idan Rejwan Christoffer Lee Ophir Sprinzak Sebastian Leks

GPUTejas v2.0 [State-of-the-art GPU Simulator] [Download Link:] https://lnkd.in/gcyVik7K We are happy to announce the public release of our GPU simulator GPUTejas v2.0 that is a part of our Tejas(R) simulation toolkit. It is freely available under the Apache 2.0 license. The previous version 1.1 had the following issues: 1. It was only compatible with Cuda Version 4.0. 2. The numbers did not match that of latest GPUs. 3. It relied on GPUOcelot for traces, whose support is only up till NVIDIA Fermi. All of this has changed with version 2.0. 1. We now use NVIDIA's NVBit tracer that is guaranteed to remain always up to date [use the latest version]. We rely on SASS traces, which produce accurate simulation results. 2. The NVIDIA Ampere pipeline is supported [The numbers match] a. Realistic streaming multiprocessor (SM), PB, TPC and GPC design 3. Supports up to CUDA 11 (for sure) on all versions of Linux 4. Memory hierarchy improvements including support for prefetching. 5. Improved warp scheduling and execution pipelines.

Polars is rapidly growing in popularity so it's super exciting to see the RAPIDS and Polars teams collaborate to add GPU acceleration! You can expect a performance boost up to 13x compared to Polars on CPU on compute-bound queries.

Adding A GPU node to a #K3S Cluster https://lnkd.in/dTX-YwRD + https://lnkd.in/dfc5vNvC An interesting combination.

In this two-minute demonstration, I'll be showing the Google Gemma, Microsoft Phi3, and Meta LLAMA3 models, all running locally on my personal computer. You will be able to observe the impressive speed at which these models operate on my system, and see how this performance compares to that of ChatGPT (GPT4). My PC setup includes an AMD Ryzen 7 5800 processor, 32GB of RAM, and an NVIDIA GeForce 3060 with 12GB of GPU RAM. For software, I'm utilizing Ollama, Docker, and Open WebUI. #gemma #phi3 #llama3 #openwebui #ollama

Tutorial: Deploy the Nvidia GPU Operator on Kubernetes Based on containerd Runtime #WorkSmartWithK8s #kubernetes #containerd #nvidiagpuoperator #tutorial https://lnkd.in/e9T_V3z9

Announcing NVIDIA Magnum IO NVSHMEM 3.0, a parallel programming interface that provides efficient and scalable communication for NVIDIA GPU clusters. New features include: ✅ Multi-node, multi-interconnect support ✅ Host-device ABI backward compatibility ✅ CPU-assisted InfiniBand GPU Direct Async (IBGDA) Learn more now. #DataCenter #CUDA #HPC